Artificial Intelligence has proven to be a great tool for helping radiologists and pathologists in diagnosing patients, and, ultimately, selecting the best possible patient-specific treatment. Computers can analyse digital images at an unmet speed and can detect patterns that are missed by medical experts. The development and application of artificial intelligence in medical imaging has sped up due to (1) widely available digital medical images, (2) freely available machine learning tools, and (3) high computing performance and GPU’s in particular. This combination has led to applications where computers are highly accurate in detecting patterns, and lesions to support diagnosis and prognosis. AI is used over the entire front of medical imaging, including designing optimal image acquisition schemes, acceleration of imaging, reconstruction of imaging, image enhancement, segmentation and classification.
In this UvA/VU MSc course, we will focus on applying deep learning for digital medical image acquisition, processing and automatic analysis of images. The course will introduce the basic concepts of deep learning. Students will get hands-on experience in using the most common deep neural networks that are used in medical imaging, including convolutional neural networks such as U-net. Ultimately, the core of the course will focus on combining your technical background and knowledge about physics to allow you to apply and enhance deep learning approaches for medical imaging.